{"id":"W2305847513","doi":"10.1049/iet-gtd.2015.0927","title":"Synchrophasor measurement‐based fault location technique for multi‐terminal multi‐section non‐homogeneous transmission lines","year":2016,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; University of Saskatchewan","funders":"","keywords":"Fault (geology); Phasor; Fault indicator; Electric power transmission; Overhead (engineering); Line (geometry); Stuck-at fault; Transmission line; Terminal (telecommunication); Computation; Fault coverage; Engineering; Fault detection and isolation; Power (physics); Computer science; Real-time computing; Electric power system; Algorithm; Electrical engineering; Electronic circuit; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007740181,0.0005185762,0.0003678443,0.0002210877,0.0004497003,0.00009476529,0.000190868,0.0005636818,0.00004789496],"category_scores_gemma":[0.00008817635,0.0004266277,0.0002521916,0.0004538221,0.00005179687,0.0004975828,0.000005649359,0.0001848559,0.00002369972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009580819,"about_ca_system_score_gemma":0.0001559787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002703547,"about_ca_topic_score_gemma":0.00003497876,"domain_scores_codex":[0.9971198,0.0001529756,0.0009114638,0.0006594736,0.0006457437,0.0005105711],"domain_scores_gemma":[0.9982652,0.00004132148,0.0001754113,0.0003851189,0.0008648988,0.0002680461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001326095,0.0001600742,0.00001716775,0.0002903309,0.0000302921,0.000002743582,0.00005594301,0.01097292,0.8624771,0.000004659336,0.00131413,0.124542],"study_design_scores_gemma":[0.001961196,0.000155605,0.0001755896,0.0003222203,0.00005771912,0.00002477851,0.000006855622,0.4076038,0.5597848,0.000004209122,0.02954642,0.0003567732],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01217414,0.0004663438,0.982336,0.0002186284,0.001392233,0.002345068,0.0002861747,0.0007734181,0.000007966416],"genre_scores_gemma":[0.9773083,0.0001067637,0.01936129,0.00002240256,0.0005571373,0.001476831,0.000949165,0.0001017635,0.0001163176],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9651342,"threshold_uncertainty_score":0.9998186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03596712781754841,"score_gpt":0.2690949659322715,"score_spread":0.2331278381147231,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}